In recent years, the rational utilization of saline water resources for agricultural irrigation has emerged as an effective strategy to alleviate water scarcity. To safely and efficiently exploit saline water resource...In recent years, the rational utilization of saline water resources for agricultural irrigation has emerged as an effective strategy to alleviate water scarcity. To safely and efficiently exploit saline water resources over the long term, it is crucial to understand the effects of salinity on crops and develop optimal water-salinity irrigation strategies for processing tomatoes. A two-year field experiment was conducted in 2018 and 2019 to explore the impact of water salinity levels(S1: 1 g L^(–1), S2: 3 g L^(–1), and S3: 5 g L^(–1)) and irrigation amounts(W1: 305 mm, W2: 485 mm, and W3: 611 mm) on the soil volumetric water content and soil salinity, as well as processing tomato growth, yield, and water use efficiency. The results showed that irrigation with low to moderately saline water(<3 g L^(–1)) enhanced plant wateruptake and utilization capacity, with the soil water content(SWC) reduced by 6.5–7.62% and 10.52–13.23% for the S1 and S2 levels, respectively, compared to the S3 level in 2018. Under S1 condition, the soil salt content(SSC) accumulation rate gradually declined with an increase in the irrigation amount. For example, W3 decreased by 85.00 and 77.94% compared with W1 and W2 in 2018, and by 82.60 and 73.68% in 2019, respectively. Leaching effects were observed at the W3 level under S1, which gradually diminished with increasing water salinity and duration. In 2019, the salt contents of soil under each of the treatments increased by 10.81–89.72% compared with the contents in 2018. The yield of processing tomatoes increased with an increasing irrigation amount and peaked in the S1W3 treatment for the two years, reaching 125,304.85 kg ha^(–1)in 2018 and 128,329.71 kg ha^(–1)in 2019. Notably, in the first year, the S2W3 treatment achieved relatively high yields, exhibiting only a 2.85% reduction compared to the S1W3 treatment. However, the yield of the S2W3 treatment declined significantly in two years, and it was 15.88% less than that of the S1W3 treatment. Structural equation modeling(SEM) revealed that soil environmental factors(SWC and SSC) directly influence yield while also exerting indirect impacts on the growth indicators of processing tomatoes(plant height, stem diameter, and leaf area index). The TOPSIS method identified S1W3, S1W2, and S2W2 as the top three treatments. The single-factor marginal effect function also revealed that irrigation water salinity contributed to the composite evaluation scores(CES) when it was below 0.96 g L^(–1). Using brackish water with a salinity of 3 g L^(–1)at an irrigation amount of 485 mm over one year ensured that processing tomatoes maintained high yields with a relatively high CES(0.709). However, using brackish water for more than one year proved unfeasible.展开更多
Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to ac...Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.展开更多
Drought has been analyzed by various indices based on rainfall, temperature, evapotranspiration or soil moisture data. Remoste-sensing based data has spatial continuity with certain resolution and is useful for drough...Drought has been analyzed by various indices based on rainfall, temperature, evapotranspiration or soil moisture data. Remoste-sensing based data has spatial continuity with certain resolution and is useful for drought monitor. The purpose of this study was to investigate quality used of grid relative soil moisture, the spatial and temporal variation of soi moisture, and soil moisture drought based on relative soil moisture over China during 2008-2016. The results show that the relative soil moisture data set can reflect the spatial characteristics of the development of drought in China during 2008-2016. From the spatial distribution analysis, the northwest to northeast, south part of China, and other major arid areas, the performance is particularly evident. The results show that the use of CLDAS V1.0 real-time products, access to time and space continuous soil relative humidity products, can achieve the drought in China real-time展开更多
基金funded by the National Key R&D Program of China (2022YFD1900405)。
文摘In recent years, the rational utilization of saline water resources for agricultural irrigation has emerged as an effective strategy to alleviate water scarcity. To safely and efficiently exploit saline water resources over the long term, it is crucial to understand the effects of salinity on crops and develop optimal water-salinity irrigation strategies for processing tomatoes. A two-year field experiment was conducted in 2018 and 2019 to explore the impact of water salinity levels(S1: 1 g L^(–1), S2: 3 g L^(–1), and S3: 5 g L^(–1)) and irrigation amounts(W1: 305 mm, W2: 485 mm, and W3: 611 mm) on the soil volumetric water content and soil salinity, as well as processing tomato growth, yield, and water use efficiency. The results showed that irrigation with low to moderately saline water(<3 g L^(–1)) enhanced plant wateruptake and utilization capacity, with the soil water content(SWC) reduced by 6.5–7.62% and 10.52–13.23% for the S1 and S2 levels, respectively, compared to the S3 level in 2018. Under S1 condition, the soil salt content(SSC) accumulation rate gradually declined with an increase in the irrigation amount. For example, W3 decreased by 85.00 and 77.94% compared with W1 and W2 in 2018, and by 82.60 and 73.68% in 2019, respectively. Leaching effects were observed at the W3 level under S1, which gradually diminished with increasing water salinity and duration. In 2019, the salt contents of soil under each of the treatments increased by 10.81–89.72% compared with the contents in 2018. The yield of processing tomatoes increased with an increasing irrigation amount and peaked in the S1W3 treatment for the two years, reaching 125,304.85 kg ha^(–1)in 2018 and 128,329.71 kg ha^(–1)in 2019. Notably, in the first year, the S2W3 treatment achieved relatively high yields, exhibiting only a 2.85% reduction compared to the S1W3 treatment. However, the yield of the S2W3 treatment declined significantly in two years, and it was 15.88% less than that of the S1W3 treatment. Structural equation modeling(SEM) revealed that soil environmental factors(SWC and SSC) directly influence yield while also exerting indirect impacts on the growth indicators of processing tomatoes(plant height, stem diameter, and leaf area index). The TOPSIS method identified S1W3, S1W2, and S2W2 as the top three treatments. The single-factor marginal effect function also revealed that irrigation water salinity contributed to the composite evaluation scores(CES) when it was below 0.96 g L^(–1). Using brackish water with a salinity of 3 g L^(–1)at an irrigation amount of 485 mm over one year ensured that processing tomatoes maintained high yields with a relatively high CES(0.709). However, using brackish water for more than one year proved unfeasible.
基金supported by the National Natural Science Foundation of China(42101382 and 41901342)the Shandong Provincial Natural Science Foundation(ZR2020QD016)the National Key Research and Development Program of China(2016YFD0300101).
文摘Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.
文摘Drought has been analyzed by various indices based on rainfall, temperature, evapotranspiration or soil moisture data. Remoste-sensing based data has spatial continuity with certain resolution and is useful for drought monitor. The purpose of this study was to investigate quality used of grid relative soil moisture, the spatial and temporal variation of soi moisture, and soil moisture drought based on relative soil moisture over China during 2008-2016. The results show that the relative soil moisture data set can reflect the spatial characteristics of the development of drought in China during 2008-2016. From the spatial distribution analysis, the northwest to northeast, south part of China, and other major arid areas, the performance is particularly evident. The results show that the use of CLDAS V1.0 real-time products, access to time and space continuous soil relative humidity products, can achieve the drought in China real-time
